Spurious correlation #3,261 · View random
A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Aurora and the second variable is Wind power generated in Poland. The chart goes from 1995 to 2021, and the two variables track closely in value over that time.

A linear line chart with years as the X-axis and two variables on the Y-axis. The first variable is Popularity of the first name Aurora and the second variable is Wind power generated in Poland. The chart goes from 1995 to 2021, and the two variables track closely in value over that time.

Download png, svg

AI explanation

Every time someone named Aurora was born, a tiny burst of excitement surged through the air, inadvertently nudging wind turbines in Poland to spin just a little bit faster. The correlation was undeniable - it seemed that the key to accelerating wind power lay in embracing the name that quite literally brought a breath of fresh air wherever it went.

^{Show image generation prompt} Model: dalle-3
Prompt: [ An image that looks like it is a scene from a Pixar movie ] Generate an image of a serene and picturesque field in the Polish countryside. In the foreground, tall wind turbines stand prominently, their blades slowly rotating with grace against the vibrant green landscape. The sky above is illuminated by a soft, colorful glow, creating intricate swirling patterns that seem to dance around the turbines. In the distance, families are seen joyously celebrating the arrival of newborns named Aurora, unknowingly contributing to the gentle but powerful winds that now drive the turbines. The air is filled with the soothing sound of the turbines whirring, while a sense of collective magic lingers in the air, as if the name Aurora has become an integral part of the renewable energy landscape, weaving its way into the very fabric of the wind. The depiction should include only scenes, people, and objects, it should not include text or speech bubbles.

This image generation prompt was also generated by AI, using the following sequence:

Model: gpt-3.5-turbo-1106
System prompt: You select one of a few potential scene descriptors that seems to go along with the variables described. You respond only with the scene descriptions provided and say nothing else.
User prompt: Here are two variables: Popularity of the first name Aurora and Wind power generated in Poland. Given these, which of the following image style descriptions seems most appropriate? Pick the one that seems to be related to one of the variables at hand. Please respond with only one of the following potential descriptions: Vaporwave Photorealism Macabre Cute cartoon An image that looks like it is a scene from a Pixar movie
ChatGPT: An image that looks like it is a scene from a Pixar movie

Model: gpt-3.5-turbo-1106
System prompt: You describe simple visual scenes that depict the two variables stated. You combine both variables into one scene.
User prompt: Please describe a visual scene that prominently depicts both Popularity of the first name Aurora and Wind power generated in Poland, in the following situation: As the name Aurora gained popularity, more parents subconsciously summoned wind energy with their baby-naming prowess. The collective "Aurora" chant created a gusty shift, powering up wind turbines across Poland. Baby names: the new renewable resource!. Keep the description simple.
ChatGPT: In a picturesque field in Poland, wind turbines stand tall, their blades spinning gracefully. Above them, a soft, colorful glow emanates, creating swirling patterns in the sky. At the same time, parents across the country are choosing the name Aurora for their newborns, unknowingly and collectively conjuring the gentle but powerful winds that are now driving the turbines. The air is filled with the sound of the turbines whirring, and the name Aurora seems to permeate the very essence of the wind, becoming a part of the renewable energy landscape in a beautiful and unexpected way.

Model: gpt-3.5-turbo-1106
System prompt: You write and improve image generation prompts for DALLE-3. You maintain the same subjects, but enhance the visuals.
User prompt: Please respond with an image generation prompt that would be likely to create an interesting visual using DALLE-3, using the following style: [ An image that looks like it is a scene from a Pixar movie ] and depicting this scene: In a picturesque field in Poland, wind turbines stand tall, their blades spinning gracefully. Above them, a soft, colorful glow emanates, creating swirling patterns in the sky. At the same time, parents across the country are choosing the name Aurora for their newborns, unknowingly and collectively conjuring the gentle but powerful winds that are now driving the turbines. The air is filled with the sound of the turbines whirring, and the name Aurora seems to permeate the very essence of the wind, becoming a part of the renewable energy landscape in a beautiful and unexpected way. Keep the visual depiction simple. Do not include any words or speech bubbles.
ChatGPT: [ An image that looks like it is a scene from a Pixar movie ] Generate an image of a serene and picturesque field in the Polish countryside. In the foreground, tall wind turbines stand prominently, their blades slowly rotating with grace against the vibrant green landscape. The sky above is illuminated by a soft, colorful glow, creating intricate swirling patterns that seem to dance around the turbines. In the distance, families are seen joyously celebrating the arrival of newborns named Aurora, unknowingly contributing to the gentle but powerful winds that now drive the turbines. The air is filled with the soothing sound of the turbines whirring, while a sense of collective magic lingers in the air, as if the name Aurora has become an integral part of the renewable energy landscape, weaving its way into the very fabric of the wind.

*Variations in the User Prompts from chart to chart are just the result of random number generation in Python. I wrote a few arrays of various styles and methods to ask questions to change up the results. Every time this site writes an explanation or generates an image, the script picks from each at random.

I sequence the requests into multiple prompts because I find GPT 3.5 to perform much better with short, well-managed contexts. Thus, I track the context directly in Python and only ask ChatGPT targeted questions.
^{Show prompt for explanation} Model: gpt-3.5-turbo-1106 (fine-tuned)
System prompt: You provide humorous responses in the form of plausible sounding explanations for correlations. You assume the correlation is causative for the purpose of the explanation even if it is ridiculous. You do not chat with the user, you only reply with the causal connection explanation and nothing else.
User prompt: Please make up a funny explanation for how an increase in Popularity of the first name Aurora caused Wind power generated in Poland to increase.

AI academic paper

(Because p < 0.01)

Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland

Journal of Renewable Energy Aesthetics

Jan 2024

Reminder: This paper is AI-generated. Not real!

^{Show prompt used to generate this paper} Quick note: This sequence is long and repetitive. That's how the prompt engineering works for this one. I manage the output in Python to format it into this PDF.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft a literature review section of an academic research paper, that starts out dry for the first few sentences but then becomes hilarious and goofy.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up a literature review section of an academic research paper presenting the findings of the connection between Popularity of the first name Aurora and Wind power generated in Poland. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Speak in the present tense for this section (the authors find...), as is common in academic research paper literature reviews. Name the sources in a format similar to this: In "Book," the authors find lorem and ipsum.

Make up the lorem and ipsum part, but make it sound related to the topic at hand.

Start by naming serious-sounding studies by authors like Smith, Doe, and Jones - but then quickly devolve. Name some real non-fiction books that would be related to the topic. Then name some real fiction books that sound like they could be related. Then you might move on to cartoons and children's shows that you watched for research.

Here is the title and abstract of the paper:
[[TITLE]]
Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland

[[ABSTRACT]]
In this paper, we endeavor to explore the curious correlation between the popularity of the first name Aurora and the wind power generated in Poland. Despite the seemingly whimsical nature of this inquiry, our research, utilizing data from the US Social Security Administration and the Energy Information Administration, has yielded fascinating results. Our findings reveal a remarkably strong correlation coefficient of 0.9891767 and p < 0.01, spanning the years 1995 to 2021.

While this investigation may appear to be nothing more than a flight of fancy, our rigorous statistical analysis has brought to light a compelling relationship that cannot be simply blown away. We cautiously posit that there might indeed be an uncharted harmony between the rise of the name Aurora and the generation of wind power in Poland. Our hope is that this study will shed light on the curious interplay between nomenclature trends and renewable energy sources, and open the door to further exploration of this whimsical yet potentially impactful phenomenon.

-------

And here is the introduction section that led to this literature review:
[[INTRODUCTION]]
The intersection of nomenclature trends and renewable energy sources has long been overlooked in the annals of academic research. While the correlation between traditional economic indicators and energy generation has been extensively explored, the potential influence of baby names on renewable power remains a relatively uncharted territory.
In this paper, we delve into the intriguing relationship between the meteoric rise in popularity of the first name Aurora and the wind power generation in Poland. Initially, the seemingly whimsical nature of this endeavor might raise a few eyebrows, but as we navigate through our findings, it becomes evident that there is more than meets the eye. The alluring charm of this inquiry lies not only in its unexpected nature but also in the potential implications it holds for the interdisciplinary realms of sociology, psychology, and energy studies.
As we embark on this scholarly expedition, we must acknowledge the pioneering spirit that propels us forward, resembling the gusto of a lone wind turbine in a vast expanse of open fields. Embracing the winds of curiosity, we set sail to unravel the enigmatic bond between the ethereal name Aurora and the gusts of wind that power turbines in distant lands. While the initial reaction to this investigation might be one of mild amusement, we assure our esteemed readers that our approach is rooted in the soil of rigor and empirical inquiry.
So, let us journey into the realm where whimsy meets empirical analysis, where the serendipitous dance of data illuminates unexpected connections, and where a name, as fair and radiant as Aurora, may hold sway over the winds of change in the Polish energy landscape. With an inquisitive spirit and a touch of delight, we invite you to join us in uncovering the secrets of "Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland."

-------

Your response should start like this:

[[LITERATURE REVIEW]]

ChatGPT:
[[LITERATURE REVIEW]]

The nexus between nomenclature trends and renewable energy sources has been a topic of limited exploration within the scholarly community. While the vast majority of research has focused on economic, environmental, and policy factors influencing the generation of renewable energy, there has been a dearth of investigation into the potential impact of popular first names on such energy production.

In the seminal work of Smith and Doe (2008), the influence of cultural phenomena on energy consumption patterns is analyzed, albeit without delving into the specific realm of names and their potential correlation with wind power generation. The authors posit that societal trends and cultural shifts play a significant role in shaping energy demand but stop short of connecting these trends to the popular first name Aurora, which has seen a remarkable surge in usage in recent years.

Furthermore, Jones (2012) presented a comprehensive analysis of renewable energy adoption and transition dynamics in Eastern Europe, with a particular focus on the role of government policies and technological advancements. However, the influence of individual names on the renewable energy landscape remains conspicuously absent from the discourse, prompting the present investigation into the enigmatic relationship between the name Aurora and wind power generation in Poland.

Turning to related literature in the domain of sociology and cultural studies, 'Names and Identities: Exploring the Significance of Personal Names' by Johnson and Smith (2015) offers insights into the cultural significance attached to personal names. While the book predominantly explores the impact of names on identity formation and social interactions, it sets the stage for the consideration of names in unusual contexts, such as their potential association with renewable energy sources.

In a similar vein, the fictional works of J.K. Rowling, particularly the Harry Potter series, introduce a character named Aurora Sinistra, a professor of Astronomy at Hogwarts School of Witchcraft and Wizardry. Although the setting of this tale does not directly correlate with the energy landscape of Poland, the whimsical connections that fiction can foster are not to be underestimated in exploring the connections between names and unconventional domains such as renewable energy.

Drawing from a more unconventional pool of sources, the animated series "Winx Club" features a character named Princess Aisha, whose magical abilities are intertwined with the forces of nature. While this show is not typically considered a scholarly resource, its creative depiction of characters with names evoking natural elements contributes to a broader understanding of the symbolic power that names may hold.

As we traverse this eclectic mix of literature and cultural references, it becomes apparent that while the inquiry into the relationship between the popularity of the first name Aurora and wind power generated in Poland may seem whimsical at first glance, there is a rich tapestry of unconventional connections waiting to be unraveled. This literature review sets the stage for our empirical investigation, framed by humor, curiosity, and a hint of daring as we venture into the uncharted winds of scholarly exploration.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the methodology section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the methodology section of an academic research paper presenting the findings of the connection between Popularity of the first name Aurora and Wind power generated in Poland. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Your research team collected data from all across the internet, but mostly just used information from US Social Security Administration and Energy Information Administration . You used data from 1995 to 2021

Make up the research methods you don't know. Make them a bit goofy and convoluted.

Here is the title, abstract, and introduction of the paper:
[[TITLE]]
Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland

[[ABSTRACT]]
In this paper, we endeavor to explore the curious correlation between the popularity of the first name Aurora and the wind power generated in Poland. Despite the seemingly whimsical nature of this inquiry, our research, utilizing data from the US Social Security Administration and the Energy Information Administration, has yielded fascinating results. Our findings reveal a remarkably strong correlation coefficient of 0.9891767 and p < 0.01, spanning the years 1995 to 2021.

While this investigation may appear to be nothing more than a flight of fancy, our rigorous statistical analysis has brought to light a compelling relationship that cannot be simply blown away. We cautiously posit that there might indeed be an uncharted harmony between the rise of the name Aurora and the generation of wind power in Poland. Our hope is that this study will shed light on the curious interplay between nomenclature trends and renewable energy sources, and open the door to further exploration of this whimsical yet potentially impactful phenomenon.

[[INTRODUCTION]]
The intersection of nomenclature trends and renewable energy sources has long been overlooked in the annals of academic research. While the correlation between traditional economic indicators and energy generation has been extensively explored, the potential influence of baby names on renewable power remains a relatively uncharted territory.
In this paper, we delve into the intriguing relationship between the meteoric rise in popularity of the first name Aurora and the wind power generation in Poland. Initially, the seemingly whimsical nature of this endeavor might raise a few eyebrows, but as we navigate through our findings, it becomes evident that there is more than meets the eye. The alluring charm of this inquiry lies not only in its unexpected nature but also in the potential implications it holds for the interdisciplinary realms of sociology, psychology, and energy studies.
As we embark on this scholarly expedition, we must acknowledge the pioneering spirit that propels us forward, resembling the gusto of a lone wind turbine in a vast expanse of open fields. Embracing the winds of curiosity, we set sail to unravel the enigmatic bond between the ethereal name Aurora and the gusts of wind that power turbines in distant lands. While the initial reaction to this investigation might be one of mild amusement, we assure our esteemed readers that our approach is rooted in the soil of rigor and empirical inquiry.
So, let us journey into the realm where whimsy meets empirical analysis, where the serendipitous dance of data illuminates unexpected connections, and where a name, as fair and radiant as Aurora, may hold sway over the winds of change in the Polish energy landscape. With an inquisitive spirit and a touch of delight, we invite you to join us in uncovering the secrets of "Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland."

-------

Your response should start like this:

[[METHODOLOGY]]

ChatGPT:

[[METHODOLOGY]]

Data Collection:
To commence our peculiar expedition, we embarked on a journey through the vast realm of the internet, navigating the digital landscapes in search of datasets that would shed light on the relationship between the popularity of the first name Aurora and the wind power generated in Poland. Our primary sources of data included the venerable US Social Security Administration, which meticulously records the ebb and flow of baby names, and the Energy Information Administration, a reservoir of knowledge on power generation and consumption. It should be noted that we occasionally ventured into the lesser-known corners of cyberspace, where datasets slumbered like forgotten treasures awaiting discovery, though we found no hidden troves of name-energy correlations, only memes and cat videos.

Data Processing:
The data collected were akin to the raw materials waiting to be refined in the crucible of statistical analysis. Employing arcane algorithms and abstruse statistical techniques, we transmuted the raw data into gleaming nuggets of knowledge, illuminating the mysterious connection between human nomenclature and atmospheric forces. Outliers were treated with the cautious curiosity of a lepidopterist encountering a rare butterfly, for in the realm of data analysis, one must always be vigilant against the sly antics of statistical outliers attempting to throw the entire study off course.

Statistical Analysis:
With the processed data in hand, we summoned the spirits of statistical analysis to unveil the secrets hidden within. Employing robust statistical methods, including correlation analysis and regression modeling, we probed the strands of data with the tenacity of an archeologist excavating ancient artifacts. The correlation coefficient emerged as a reliable companion on this scholarly quest, guiding our gaze to the remarkable affinity between the name Aurora and the triumphant whirl of wind turbines in Poland. The p-value, that elusive arbiter of statistical significance, stood as a sentinel against the deluge of random chance, ensuring that our findings were not mere flukes in the tempest of data.

Temporal Scope:
Our odyssey through the data landscape spanned the years 1995 to 2021, encompassing a substantial trajectory of time, akin to witnessing the growth of a sapling into a sturdy tree. The breadth of this temporal expanse allowed us to capture the undulating tides of popularity associated with the name Aurora and the inexorable march of wind power development in Poland.

Sensitivity Analysis:
As prudent stewards of scholarly inquiry, we conducted sensitivity analysis to ascertain the robustness of our findings. This involved subjecting our data to various perturbations and perturbations, akin to testing the resilience of a well-constructed sail against capricious gusts. The robustness of our results remained steadfast in the face of these provocations, which only served to bolster our confidence in the veracity of the observed phenomenon.

Ethical Considerations:
Amidst the fervor of inquiry, we remained steadfast in upholding the ethical standards of data usage and research conduct. All data were treated with the utmost respect and confidentiality, and no names were harmed in the making of this study. The anonymity of individuals behind the name Aurora was diligently safeguarded, for in the realm of research, ethical considerations must remain as unyielding as the foundations of a wind turbine.

In sum, our methodology was a concoction of data delving, statistical sorcery, and ethical fortitude, culminating in a rigorous exploration of the peculiar correlation between the name Aurora and the winds that power the turbines of Poland.

Model: gpt-3.5-turbo-1106
System prompt: You are a goofy academic researcher writing an academic paper. You follow the rules, but you throw out convention regularly to make jokes and funny asides.You draft the discussion section of an academic research paper.
User prompt:
Pretend you are an academic researcher. Your writing sounds like it belongs in a medical or economics journal. However, you frequently crack jokes, make puns, or add unexpected twists in your writing.

Please make up the discussion section of an academic research paper presenting the findings of the connection between Popularity of the first name Aurora and Wind power generated in Poland. Make it seem dry like academic research paper, but with lots of subtle hidden offhand remarks, jokes, goofy observations, and puns.

Limit your response to 500 tokens.

Here are the title, abstract, literature review, and results sections. Please harken back to 1-2 of the goofy items in the literature review, but pretend to take them completely seriously. Discuss how your results supported the prior research.

Do not write a conclusion. I will add a conclusion after this.

[[TITLE]]
Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland

[[ABSTRACT]]
In this paper, we endeavor to explore the curious correlation between the popularity of the first name Aurora and the wind power generated in Poland. Despite the seemingly whimsical nature of this inquiry, our research, utilizing data from the US Social Security Administration and the Energy Information Administration, has yielded fascinating results. Our findings reveal a remarkably strong correlation coefficient of 0.9891767 and p < 0.01, spanning the years 1995 to 2021.

While this investigation may appear to be nothing more than a flight of fancy, our rigorous statistical analysis has brought to light a compelling relationship that cannot be simply blown away. We cautiously posit that there might indeed be an uncharted harmony between the rise of the name Aurora and the generation of wind power in Poland. Our hope is that this study will shed light on the curious interplay between nomenclature trends and renewable energy sources, and open the door to further exploration of this whimsical yet potentially impactful phenomenon.

[[LITERATURE REVIEW]]
The nexus between nomenclature trends and renewable energy sources has been a topic of limited exploration within the scholarly community. While the vast majority of research has focused on economic, environmental, and policy factors influencing the generation of renewable energy, there has been a dearth of investigation into the potential impact of popular first names on such energy production.
In the seminal work of Smith and Doe (2008), the influence of cultural phenomena on energy consumption patterns is analyzed, albeit without delving into the specific realm of names and their potential correlation with wind power generation. The authors posit that societal trends and cultural shifts play a significant role in shaping energy demand but stop short of connecting these trends to the popular first name Aurora, which has seen a remarkable surge in usage in recent years.
Furthermore, Jones (2012) presented a comprehensive analysis of renewable energy adoption and transition dynamics in Eastern Europe, with a particular focus on the role of government policies and technological advancements. However, the influence of individual names on the renewable energy landscape remains conspicuously absent from the discourse, prompting the present investigation into the enigmatic relationship between the name Aurora and wind power generation in Poland.
Turning to related literature in the domain of sociology and cultural studies, 'Names and Identities: Exploring the Significance of Personal Names' by Johnson and Smith (2015) offers insights into the cultural significance attached to personal names. While the book predominantly explores the impact of names on identity formation and social interactions, it sets the stage for the consideration of names in unusual contexts, such as their potential association with renewable energy sources.
In a similar vein, the fictional works of J.K. Rowling, particularly the Harry Potter series, introduce a character named Aurora Sinistra, a professor of Astronomy at Hogwarts School of Witchcraft and Wizardry. Although the setting of this tale does not directly correlate with the energy landscape of Poland, the whimsical connections that fiction can foster are not to be underestimated in exploring the connections between names and unconventional domains such as renewable energy.
Drawing from a more unconventional pool of sources, the animated series "Winx Club" features a character named Princess Aisha, whose magical abilities are intertwined with the forces of nature. While this show is not typically considered a scholarly resource, its creative depiction of characters with names evoking natural elements contributes to a broader understanding of the symbolic power that names may hold.
As we traverse this eclectic mix of literature and cultural references, it becomes apparent that while the inquiry into the relationship between the popularity of the first name Aurora and wind power generated in Poland may seem whimsical at first glance, there is a rich tapestry of unconventional connections waiting to be unraveled. This literature review sets the stage for our empirical investigation, framed by humor, curiosity, and a hint of daring as we venture into the uncharted winds of scholarly exploration.

[[RESULTS]]
The data analysis revealed a striking correlation between the popularity of the first name Aurora and the wind power generated in Poland. Over the period of 1995 to 2021, our statistical analysis uncovered a correlation coefficient of 0.9891767, indicating an incredibly strong positive relationship between these two variables. The r-squared value of 0.9784705 further signifies that a substantial portion of the variation in wind power generation can be attributed to the popularity of the name Aurora. With a p-value of less than 0.01, we can confidently reject the null hypothesis and assert that the observed correlation is statistically significant.
Did our team stumble upon an obscure form of renewable energy harnessed through the mere mention of the name Aurora? Or is there a mysterious aura surrounding this particular name that seems to encourage the gentle whispers of the wind to propel turbines in Poland?
In Fig. 1, a scatterplot illustrates the compelling relationship between the popularity of the name Aurora and wind power generation in Poland. The data points form a tight cluster, almost as if the winds of fate themselves have conspired to align these two seemingly disparate phenomena.
While the exact mechanisms underlying this correlation remain as enigmatic as the name itself, our findings raise thought-provoking questions about the influence of nomenclature trends on the utilization of renewable energy sources. Could it be that the very mention of the name Aurora has the subtle power to conjure up breezes that turn wind turbines in a distant land? Or is this a whimsical coincidence that tickles the fancy of statistical analysis?
One thing is for certain: this research has blown wide open a unique avenue for exploration at the intersection of linguistics and sustainable energy. As we continue to unravel the inexplicable tie between the name Aurora and wind power in Poland, it is our hope that this study will spark further inquiries into the uncharted territories where name trends and renewable resources intersect.
In conclusion, the correlation between the popularity of the first name Aurora and wind power generation in Poland merits further scrutiny and may lead to unexpected insights at the crossroads of nomenclature and sustainable energy. The winds of curiosity continue to propel us forward in this captivating journey of discovery.

-------

Your response should start like this:

[[DISCUSSION]]

ChatGPT:

[[DISCUSSION]]

Our investigation has unfurled a delightful revelation, akin to a gentle zephyr sweeping through the corridors of conventional research. The robust correlation observed between the burgeoning popularity of the name Aurora and the wind power generated in Poland presents a paradoxically whimsical yet empirically substantial conundrum. Intriguingly, our results echo the peculiar yet auspicious inklings that pepper the literature review, encapsulating the profound impact of seemingly fanciful elements on the practical realm of renewable energy production.

The findings of our study resonate with scholarly works that have dared to venture into the uncharted territories of unconventional correlations. Just as the enchanting narrative of fictional characters with names imbued with natural elements has teased our imaginations, our empirical analysis unravels a tangible link between the ethereal name Aurora and the tangible gusts of wind powering turbines in Poland. The correlation coefficient of 0.9891767, coupled with a minuscule p-value, provides compelling evidence that there exists a significant association between the ebb and flow of the name Aurora and the winds that propel sustainable energy generation in Poland.

Strikingly, our results bolster the contention that there may indeed be a sturdy undercurrent of stylistic winds influencing the emblematic landscape of renewable energy. The minute variation in wind power generation attributable to the popularity of the name Aurora, as evidenced by the high r-squared value, captures the essence of this unforeseen relationship—akin to the capricious yet calculated dance of wind currents shaping the renewable energy horizon.

The scatterplot visually encapsulates the magnetic pull between the name Aurora and the wind power yielded in Poland, offering a whimsical tableau that beckons us to ponder the mysterious forces at play. Indeed, the unspoken narrative of this correlation flirts with arcane possibilities, positing the inconceivable notion that language itself might hold sway over the elemental forces that sustain our modern energy landscape.

As we are drawn deeper into the spellbinding intersection of nomenclature and renewable energy, it becomes evident that this unexpected marriage of linguistics and sustainable resources elicits both scholarly curiosity and a touch of whimsy. Our research, while firmly anchored in statistical rigor, unfurls a fanciful fabric of inquiry, inviting further exploration into the unmistakable yet enigmatic liaison between the name Aurora and wind power generation in Poland. This profound correlation mingles with a subtle whimsy that tickles the fancy of scientific investigation, beckoning us to unravel the evocative secrets laced within the fabric of nomenclature and renewable energy dynamics.

In light of this beguiling revelation, it is imperative that we heed the heed the gentle whisper of curiosity, igniting further academic voyages into the gossamer threads of linguistic influence on the sustainable energy paradigm. It is our hope that this investigation serves as an ode to the enchanting interplay between the name Aurora and the burgeoning winds of sustainable energy, inspiring future scholars to tread the unmarked pathways of this unconventional yet undeniably captivating correlation.

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of academic journals. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding research, scientific, or academic journal that would publish a paper entited Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland. Respond only with the name of a fictional academic journal.

ChatGPT:

Journal of Renewable Energy Aesthetics

Model: gpt-3.5-turbo-1106
System prompt: You provide fictional names of nonprofit research organizations and associations. You say nothing else, and you do not chat with the user.
User prompt: Please provide the name of a funny but plausible sounding nonprofit, academic, or scientific association or organization that would sponsor research that resulted in a paper entited Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland. Respond only with the name of a fictional organization.

ChatGPT:

The Society for Aesthetic Applications of Renewable Energy

Model: gpt-3.5-turbo-1106
System prompt: You provide potential search keywords separated by commas. You say nothing else, and you do not chat with the user.
User prompt: Please provide potential search keywords for a paper titled Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland with an abstract of In this paper, we endeavor to explore the curious correlation between the popularity of the first name Aurora and the wind power generated in Poland. Despite the seemingly whimsical nature of this inquiry, our research, utilizing data from the US Social Security Administration and the Energy Information Administration, has yielded fascinating results. Our findings reveal a remarkably strong correlation coefficient of 0.9891767 and p < 0.01, spanning the years 1995 to 2021.
While this investigation may appear to be nothing more than a flight of fancy, our rigorous statistical analysis has brought to light a compelling relationship that cannot be simply blown away. We cautiously posit that there might indeed be an uncharted harmony between the rise of the name Aurora and the generation of wind power in Poland. Our hope is that this study will shed light on the curious interplay between nomenclature trends and renewable energy sources, and open the door to further exploration of this whimsical yet potentially impactful phenomenon.

ChatGPT:

Aurora popularity, wind power Poland, correlation coefficient, US Social Security Administration data, Energy Information Administration data, nomenclature trends, renewable energy sources, Poland wind energy, Aurora name correlation, statistical analysis, aesthetic alignment, Poland renewable energy, names and energy correlation

*There is a bunch of Python happening behind the scenes to turn this prompt sequence into a PDF.

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Data details

Popularity of the first name Aurora
Detailed data title: Babies of all sexes born in the US named Aurora
Source: US Social Security Administration
See what else correlates with Popularity of the first name Aurora

Wind power generated in Poland
Detailed data title: Total wind power generated in Poland in billion kWh
Source: Energy Information Administration
See what else correlates with Wind power generated in Poland

Correlation r = 0.9891767 (Pearson correlation coefficient)
Correlation is a measure of how much the variables move together. If it is 0.99, when one goes up the other goes up. If it is 0.02, the connection is very weak or non-existent. If it is -0.99, then when one goes up the other goes down. If it is 1.00, you probably messed up your correlation function.

r² = 0.9784705 (Coefficient of determination)
This means 97.8% of the change in the one variable (i.e., Wind power generated in Poland) is predictable based on the change in the other (i.e., Popularity of the first name Aurora) over the 27 years from 1995 through 2021.

p < 0.01, which is statistically significant(Null hypothesis significance test)
The p-value is 2.3E-22.^Show 0.0000000000000000000002322352
The p-value is a measure of how probable it is that we would randomly find a result this extreme.^Note More specifically the p-value is a measure of how probable it is that we would randomly find a result this extreme if we had only tested one pair of variables one time.

But I am a p-villain. I absolutely did not test only one pair of variables one time. I correlated hundreds of millions of pairs of variables. I threw boatloads of data into an industrial-sized blender to find this correlation.

Who is going to stop me? p-value reporting doesn't require me to report how many calculations I had to go through in order to find a low p-value!
On average, you will find a correaltion as strong as 0.99 in 2.3E-20% of random cases. Said differently, if you correlated 4,305,979,455,310,822,637,568 random variables^Note You don't actually need 4 sextillion variables to find a correlation like this one. I don't have that many variables in my database. You can also correlate variables that are not independent. I do this a lot.

p-value calculations are useful for understanding the probability of a result happening by chance. They are most useful when used to highlight the risk of a fluke outcome. For example, if you calculate a p-value of 0.30, the risk that the result is a fluke is high. It is good to know that! But there are lots of ways to get a p-value of less than 0.01, as evidenced by this project.

In this particular case, the values are so extreme as to be meaningless. That's why no one reports p-values with specificity after they drop below 0.01.

Just to be clear: I'm being completely transparent about the calculations. There is no math trickery. This is just how statistics shakes out when you calculate hundreds of millions of random correlations.
with the same 26 degrees of freedom, ^Note Degrees of freedom is a measure of how many free components we are testing. In this case it is 26 because we have two variables measured over a period of 27 years. It's just the number of years minus ( the number of variables minus one ), which in this case simplifies to the number of years minus one.
you would randomly expect to find a correlation as strong as this one.

[ 0.98, 1 ] 95% correlation confidence interval (using the Fisher z-transformation)
^{Read more about the confidence interval} The confidence interval is an estimate the range of the value of the correlation coefficient, using the correlation itself as an input. The values are meant to be the low and high end of the correlation coefficient with 95% confidence.

This one is a bit more complciated than the other calculations, but I include it because many people have been pushing for confidence intervals instead of p-value calculations (for example: NEJM. However, if you are dredging data, you can reliably find yourself in the 5%. That's my goal!

All values for the years included above: ^Note If I were being very sneaky, I could trim years from the beginning or end of the datasets to increase the correlation on some pairs of variables. I don't do that because there are already plenty of correlations in my database without monkeying with the years.

Still, sometimes one of the variables has more years of data available than the other. This page only shows the overlapping years. To see all the years, click on "See what else correlates with..." link above.

	1995	1996	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Popularity of the first name Aurora (Babies born)	324	355	430	503	520	557	684	664	810	989	952	1068	1068	1174	1488	1527	1726	1905	2130	2749	3648	4002	4608	4827	5001	5245	5512
Wind power generated in Poland (Billion kWh)	0.001	0	0.002	0.004	0.004	0.005	0.014	0.061	0.124	0.142	0.135	0.256	0.522	0.837	1.077	1.664	3.187	4.709	5.939	7.579	10.728	12.2786	14.5742	12.4717	14.7607	15.4292	16.181

Why this works

Data dredging: I have 25,153 variables in my database. I compare all these variables against each other to find ones that randomly match up. That's 632,673,409 correlation calculations! This is called “data dredging.” Instead of starting with a hypothesis and testing it, I instead abused the data to see what correlations shake out. It’s a dangerous way to go about analysis, because any sufficiently large dataset will yield strong correlations completely at random.
Lack of causal connection: There is probably^Note Because these pages are automatically generated, it's possible that the two variables you are viewing are in fact causually related. I take steps to prevent the obvious ones from showing on the site (I don't let data about the weather in one city correlate with the weather in a neighboring city, for example), but sometimes they still pop up. If they are related, cool! You found a loophole.
no direct connection between these variables, despite what the AI says above. This is exacerbated by the fact that I used "Years" as the base variable. Lots of things happen in a year that are not related to each other! Most studies would use something like "one person" in stead of "one year" to be the "thing" studied.
Observations not independent: For many variables, sequential years are not independent of each other. If a population of people is continuously doing something every day, there is no reason to think they would suddenly change how they are doing that thing on January 1. A simple^Note Personally I don't find any p-value calculation to be 'simple,' but you know what I mean.
p-value calculation does not take this into account, so mathematically it appears less probable than it really is.
Confounding variable: 2020 is particularly different from the other years on this graph. Confounding variables (like global pandemics) will cause two variables to look connected when in fact a "sneaky third" variable is influencing both of them behind the scenes.

Try it yourself

You can calculate the values on this page on your own! Try running the Python code to see the calculation results. ^{Show the steps to do this.} Step 1: Download and install Python on your computer.

Step 2: Open a plaintext editor like Notepad and paste the code below into it.

Step 3: Save the file as "calculate_correlation.py" in a place you will remember, like your desktop. Copy the file location to your clipboard. On Windows, you can right-click the file and click "Properties," and then copy what comes after "Location:" As an example, on my computer the location is "C:\Users\tyler\Desktop"

Step 4: Open a command line window. For example, by pressing start and typing "cmd" and them pressing enter.

Step 5: Install the required modules by typing "pip install numpy", then pressing enter, then typing "pip install scipy", then pressing enter.

Step 6: Navigate to the location where you saved the Python file by using the "cd" command. For example, I would type "cd C:\Users\tyler\Desktop" and push enter.

Step 7: Run the Python script by typing "python calculate_correlation.py"

If you run into any issues, I suggest asking ChatGPT to walk you through installing Python and running the code below on your system. Try this question:

"Walk me through installing Python on my computer to run a script that uses scipy and numpy. Go step-by-step and ask me to confirm before moving on. Start by asking me questions about my operating system so that you know how to proceed. Assume I want the simplest installation with the latest version of Python and that I do not currently have any of the necessary elements installed. Remember to only give me one step per response and confirm I have done it before proceeding."

# These modules make it easier to perform the calculation
import numpy as np
from scipy import stats

# We'll define a function that we can call to return the correlation calculations
def calculate_correlation(array1, array2):

    # Calculate Pearson correlation coefficient and p-value
    correlation, p_value = stats.pearsonr(array1, array2)

    # Calculate R-squared as the square of the correlation coefficient
    r_squared = correlation**2

    return correlation, r_squared, p_value

# These are the arrays for the variables shown on this page, but you can modify them to be any two sets of numbers
array_1 = np.array([324,355,430,503,520,557,684,664,810,989,952,1068,1068,1174,1488,1527,1726,1905,2130,2749,3648,4002,4608,4827,5001,5245,5512,])
array_2 = np.array([0.001,0,0.002,0.004,0.004,0.005,0.014,0.061,0.124,0.142,0.135,0.256,0.522,0.837,1.077,1.664,3.187,4.709,5.939,7.579,10.728,12.2786,14.5742,12.4717,14.7607,15.4292,16.181,])
array_1_name = "Popularity of the first name Aurora"
array_2_name = "Wind power generated in Poland"

# Perform the calculation
print(f"Calculating the correlation between {array_1_name} and {array_2_name}...")
correlation, r_squared, p_value = calculate_correlation(array_1, array_2)

# Print the results
print("Correlation Coefficient:", correlation)
print("R-squared:", r_squared)
print("P-value:", p_value)

Reuseable content

You may re-use the images on this page for any purpose, even commercial purposes, without asking for permission. The only requirement is that you attribute Tyler Vigen. ^Note Attribution can take many different forms. If you leave the "tylervigen.com" link in the image, that satisfies it just fine. If you remove it and move it to a footnote, that's fine too. You can also just write "Charts courtesy of Tyler Vigen" at the bottom of an article.

You do not need to attribute "the spurious correlations website," and you don't even need to link here if you don't want to. I don't gain anything from pageviews. There are no ads on this site, there is nothing for sale, and I am not for hire.

For the record, I am just one person. Tyler Vigen, he/him/his. I do have degrees, but they should not go after my name unless you want to annoy my wife. If that is your goal, then go ahead and cite me as "Tyler Vigen, A.A. A.A.S. B.A. J.D." Otherwise it is just "Tyler Vigen."

When spoken, my last name is pronounced "vegan," like I don't eat meat.

Full license details.
For more on re-use permissions, or to get a signed release form, see tylervigen.com/permission.

Download images for these variables:

High resolution line chart ^Note The image linked here is a Scalable Vector Graphic (SVG). It is the highest resolution that is possible to achieve. It scales up beyond the size of the observable universe without pixelating. You do not need to email me asking if I have a higher resolution image. I do not. The physical limitations of our universe prevent me from providing you with an image that is any higher resolution than this one.

If you insert it into a PowerPoint presentation (a tool well-known for managing things that are the scale of the universe), you can right-click > "Ungroup" or "Create Shape" and then edit the lines and text directly. You can also change the colors this way.

Alternatively you can use a tool like Inkscape.
High resolution line chart, optimized for mobile
Alternative high resolution line chart
Scatterplot
Portable line chart (png)
Portable line chart (png), optimized for mobile
Line chart for only Popularity of the first name Aurora
Line chart for only Wind power generated in Poland
AI-generated correlation image
The spurious research paper: Aurora's Ascendancy: Aesthetic Alignment with Wind power in Poland

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Correlation ID: 3261 · Black Variable ID: 3130 · Red Variable ID: 23904


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